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Yooho Lee
Yooho Lee
I'm M.S candidate in Intelligent Image Signal Processing Lab @ Dong-A University, advised by Prof. Dongsan Jun. Previously, I received my B.S and M.S. degrees in Information and Communication Engineering at Kyungnam University and Computer Engineering at Dong-A university, respectively.
My research interests lie in the intersection of deep learning and video coding. More specifically, my research has been focused on
Deep Learning (Image/Video)
Super Resolution
Compression Artifacts Reduction
Video Coding (International Standard of ISO/IEC MPEG, ITU-T)
High Efficiency Video Coding (HEVC)
Versatile Video Coding (VVC)
Enhanced Compression Beyond VVC Capaability (ECM, Post VVC)
Neural Network-based Video Coding (NNVC, Post VVC)
Contact
E-mail: yhlee@donga.ac.kr
Office: Image Signal Processing Lab, Room 210, 1th Engineering Building (S-03), Seunghak Campus, Dong-A University, Hadan-dong Saga-gu, Busan, Rep. of Korea (Google map)
International Journal (SCIE)
International Journal (SCIE)
Video Super-Resolution Method Using Deformable Convolution-Based Alignment Network
Video Super-Resolution Method Using Deformable Convolution-Based Alignment Network
Yooho Lee, Sukhee Cho and Dongsan Jun*, November. 2022
Published in Sensors (JCR Rank 28.91%, IF 3.9)
[doi]
Reduction of Compression Artifacts using Densely Cascading Image restoration Network
Reduction of Compression Artifacts using Densely Cascading Image restoration Network
Yooho Lee, Sang-hyo Park, Eunjun Rhee, Byung-Gyu Kim* and Dongsan Jun*, 2021
Published in Applied Sciences (JCR Rank 34.6%, IF 2.41)
[doi]
Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network
Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network
Yooho Lee, Dongsan Jun*, Byung-Gyu Kim and Hunjoo Lee, May. 2021
Published in Sensors (JCR Rank 22.6%, IF 3.28)
[doi]
Domestic Journal (KCI)
Domestic Journal (KCI)
Video Super Resolution Method using Deformable Convolution based Alignment Network with Residual Dense Block
Video Super Resolution Method using Deformable Convolution based Alignment Network with Residual Dense Block
Dongho Lee, Yooho Lee, Sejin Chun and Dongsan Jun*, May. 2023
Published in Journal of Korea Multimedia Society
[doi]
CNN based Image Restoration Method for the Reduction of Compression Artifacts
CNN based Image Restoration Method for the Reduction of Compression Artifacts
Domestic Patent
Domestic Patent
영상 부/복호화 방법 및 장치, 10-2023-0148073
영상 부/복호화 방법 및 장치, 10-2023-0148074
Lightweight Center Frame Weight Centralized Alignment Network, 10-2023-0161926
Video Super Resolution for Deformable Convolution based Center Oriented Alignment Network, 10-2022-0175152
Project Experience
Project Experience
Government-Funded Projects as Student Researcher
Research and standardization on deep learning based low complexity video coding algorithm for hyper reality video Jun. 2023 ~ Present
funded by National Research Foundation of Korea (NRF)
Development of immersive video spatial computing technology for ultra-realistic metaverse services Jan. 2022 ~ Present
funded by Electronics and Telecommunications Research Institute (ETRI)
Research on video quality enhancement using DNN based super resolution and artifacts reduction. Jan. 2022 ~ Present
funded by Electronics and Telecommunications Research Institute (ETRI)
Research on encoding/decoding method for omnidirectional LF video based on deep neural networks. Mar. 2021 ~ Nov. 2021
funded by Electronics and Telecommunications Research Institute (ETRI)
Research on image quality enhancement using DNN based super resolution. Sep. 2019 ~ Oct. 2021
funded by Electronics and Telecommunications Research Institute (ETRI)
Technical Skill
Technical Skill
Programming Languages: C/C++, Python (Pytorch, Tensorflow), MATLAB
Ref. S/W for Video Coding Standardization: HM (HEVC Test Model), VTM (VVC Test Model)
OS: Windows, Linux
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